Trip oriented energy management control

ABSTRACT

An engine, electric machine and battery of a vehicle are operated such that a state of charge of the battery generally decreases and then achieves approximately a charge-depletion-to-charge-sustaining transition threshold after the vehicle has been driven a distance greater than the pure electrical range of the vehicle.

TECHNICAL FIELD

The present disclosure is related to electric energy management in aplug-in hybrid electric vehicle.

BACKGROUND

Plug-in Electric Hybrid Vehicles (PHEV) are an extension of existinghybrid electric vehicles (HEV) with added energy flexibility.Traditional HEVs buffer fuel energy and recover kinematic energy inelectric form to improve the overall vehicle system operatingefficiency. Fuel is typically the only source of energy in an HEV. APHEV utilizes a larger capacity battery pack than a standard HEV and thePHEV has two sources of energy, fuel and electricity from the electricutility grid. Fuel is typically more expensive but readily availablewhile driving due to existing infrastructure. Electricity is lessexpensive but limited by battery capacity and charge state. Thisadditional source of energy supply adds complexity to the control systemstrategy. The control system can bias the PHEV towards electricalpropulsion to increase fuel efficiency.

The energy economy of a PHEV is derived from the PHEV system design,extended energy storage system, and the PHEV energy management controlstrategy. The PHEV energy management control (EMC) strategy is generallysimilar to that of HEVs, with the main objective of minimizing energyoperational costs and emissions without compromising the vehicledrivability and system constraints. A standard EMC strategy istraditionally designed to operate the PHEV in electric drive (EV) modeor to maximize the battery power output in blended operation mode beforethe next plug-in recharge event. The added electric energy supply andits more frequent recharge expectations increase the complexity of thePHEV energy management problem thus making the solution more challengingand complicated.

SUMMARY

A plug-in hybrid electric vehicle may include an engine, an electricmachine, a battery, and at least once controller. The at least onecontroller may operate the engine and electric machine such that a stateof charge of the battery achieves approximately acharge-depletion-to-charge-sustaining transition threshold after thevehicle has been driven a distance greater than a pure electrical rangeof the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a trip oriented energy management control system flow diagram;

FIG. 2 a illustrates an example plot of how battery state of chargevaries with trip distance in a standard plug-in hybrid control strategy;

FIG. 2 b illustrates an example plot of how battery state of chargevaries with trip distance in a plug-in hybrid system controlled using alinear SOC profile constructed with respect to recharge cycle drivingdistance;

FIG. 2 c illustrates an example plot of how battery state of chargevaries with trip distance in a plug-in hybrid system controlled using apolynomial SOC profile constructed with respect to recharge cycledriving distance;

FIG. 2 d illustrates an example plot of how battery state of chargevaries with trip distance in a plug-in hybrid system controlled using apiecewise linear Charge Depletion SOC profile and a continuouslycalculated Charge Depletion SOC profile constructed with respect torecharge cycle driving distance;

FIG. 3 is a schematic drawing of an example plug-in hybrid powertrainsystem configuration;

FIG. 4 a illustrates an example engine efficiency map in relation toengine speed versus engine torque for a PHEV;

FIG. 4 b illustrates another example engine efficiency map in relationto engine speed versus engine torque for a PHEV;

FIG. 5 is a power sourcing distribution optimization flow diagram;

FIG. 6 illustrates battery power as a function of vehicle speed anddemand power at the wheel that is determined as a function of energyconsumption ratio of fuel consumption rate to electricity consumptionrate with respect to battery SOC;

FIG. 7 is an energy management strategy flow diagram; and,

FIG. 8 illustrates an optimal engine speed map as a function of vehiclepower versus vehicle speed.

DETAILED DESCRIPTION

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention that may be embodied in variousand alternative forms. The figures are not necessarily to scale; somefeatures may be exaggerated or minimized to show details of particularcomponents. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as arepresentative basis for teaching one skilled in the art to variouslyemploy the present invention.

The PHEV energy optimization has multiple different paths; a fewdifferent paths will be discussed in this disclosure. One of thosemethods applies dynamic programming (DP) to determine the optimalpowertrain operating states and the energy consumption distributionbetween the fuel and the electricity based on detailed trip knowledge.Due to its non-causal nature and heavy computation loads, the DP basedPHEV energy management control strategy is typically evaluated offlinewhere the energy economy potential can be explored and optimized. Theinsights obtained from a DP control process can serve as a guideline formany alternative methods including rule based control design andcalibrations. There has been research comparing the performance of anelectric-centric charge-depleting hybrid vehicle control strategy with anear-optimal dynamic programming-optimized control strategy. Anotherpath utilizes online/real-time implementable PHEV energy managementcontrol rules. One method is the Equivalent Consumption MinimizationStrategy (ECMS). This strategy utilizes the concept of instantaneousequivalent fuel consumption. Theoretically based on Pontryagin's MinimumPrinciple, this method provides a metric such that the fuel energyconsumption and the battery electric energy consumption can be evaluatedsimultaneously towards a global optimization objective. There are a fewknown strategies like an adaptive ECMS control strategy thatincorporated real-time driving cycle information into the adjustment ofthe ECMS control setpoint and a stochastic optimal control based PHEVenergy management strategy. These are based on DP and may realize anoptimal energy management process using detailed trip knowledge for onespecific trip. The result, however, can not be applied online to realworld driving cycles. The afore-mentioned implementable energymanagement methods assume either no trip foreknowledge or just shortrange preview information. The result is that their optimality incontrol is only valid with respect to an averaged operator usage anddriving patterns.

Based on optimal control theory, this disclosure relates to aTrip-Oriented Energy Management Control (TEMC) strategy that includesoptimizing the trip specific PHEV energy economy based on scalable tripforeknowledge. This TEMC strategy covers the gap between the DP and therule based methods by providing a systematic control architecture thatis able to optimize PHEV energy management using limited available tripinformation. The trip oriented energy management problem can be solvedat two levels of optimization. At the higher level, i.e., the tripdomain optimization, a global energy usage/consumption optimization iscarried out such that the battery electric energy and the fuel usage ispre-planned based on scalable trip foreknowledge and energy storagestates. An optimal battery SOC depletion/usage profile is generated. Thetrip domain SOC profile serves as a feed forward guideline for the PHEVonline energy management control towards global energy economyimprovement over a given driving schedule. Next, the trip specificoptimal fuel consumption to electricity depletion ratio index isadaptively searched online through a feedback control mechanism suchthat the overall controlled energy consumption process achievesapproximately the preplanned optimal process. At the vehicle systemlevel, the most efficient PHEV system power split state and powersourcing state are optimally resolved for a specific PHEV with respectto vehicle states, system constraints and the trip domain energyconsumption ratio index.

It may be desirable to have an optimized solution that relies onknowledge about the operator's energy usage patterns and drivingpatterns. This data, however, is not always available. One goal of thePHEV energy management optimization objective may be to optimize thesystem efficiency with minimized operating power loss. It may also bedesirable to find an operator usage oriented solution that optimizes thesystem operation and the energy consumption comprehensively. Certainexamples herein focus on the trip domain feedback control and thevehicle domain system optimization. The generation of the battery SOCprofile used in this disclosure discusses the minimum level of tripforeknowledge being trip distance until next charge, but the SOC profileis not limited to trip distance and may include other tripcharacteristics such as route characteristics, real-time data, drivercharacteristics, or desired driver behavior. The route characteristicsinclude but are not limited to map information like road type (highway,city, etc.) posted speed limits, and road grade, which is a directionalchange in elevation. The real-time data includes but are not limited totraffic, construction, accidents, weather, and lane closures. The drivercharacteristics include but are not limited to historical driverpatterns, a determination if a commute is based on day of the week andtime of day. The driver desired behavior includes but is not limited todriver input (performance, economy, city, etc.) or driver demand. A PHEVenergy management strategy that incorporates the trip distanceinformation can achieve better fuel economy by allowing an extendedscale of system optimization.

A trip oriented energy management control system is shown schematicallyin FIG. 1. The powertrain control (PCM) block 100 is based on a basicpowertrain model such as:

${J_{eng}\frac{\mathbb{d}\omega_{eng}}{\mathbb{d}t}} = {\tau_{eng} + {T_{e\; 2\; g}\tau_{sun}}}$${J_{mot}\frac{\mathbb{d}\omega_{mot}}{\mathbb{d}t}} = {\tau_{mot} - {\frac{T_{1}T_{2}}{\varrho}\tau_{sun}} - {\frac{T_{2}}{T_{g}}\tau_{dft}}}$$J_{gen}\frac{\mathbb{d}\omega_{gen}}{\mathbb{d}t}$

In these equations, J₁ terms are inertias, τ_(i) terms are torques,ω_(i) is the rotational speed typically expressed in rpm, and T_(i)terms are speed and torque transfer ratios between driveline components(T_(e2g) is the gear ratio from engine to generator, T₁ is the gearratio from counter shaft to ring gear, T₂ is the gear ratio from motorshaft to countershaft, T_(g) is the gear ratio from drive shaft tocounter shaft). This can also be evaluated using angular speed, which istypically expressed as rad/sec. (Subscript i=eng; mot; gen; dft; sunindicate the engine, electric motor, electric generator, driveshaft andsun gear respectively.) These equations cover specific electric machineimplementations, namely an electric motor and an electric generator, butthe concepts are not limited to those implementations. The engine,electric motor, and electric generator each have a maximum torque. Thetotal torque available to provide vehicle propulsion is approximatelyτ_(eng)+τ_(mot) minus driveline losses. When the vehicle is operated ata point greater than τ_(mot), then both the engine and motor can beoperated to achieve the desired torque. If the desired torque is lessthan τ_(mot), then the motor alone may be used to provide the desiredtorque.

The requested torques τ_(eng), τ_(gen), τ_(mot) are communicated to thevehicle 110 and the vehicle operation data including but not limited tovehicle speed and distance traveled is communicated to the batterycharge depletion profile generator (DP) 120, which generates a referencestate of charge 122. The reference state of charge can be determinedusing multiple methods, such as linear, polynomial, preplanned piecewise linear, and preplanned continuous. The linear reference state ofcharge at a discrete trip location (SOC_(ref) _(—) _(lnr)(s)) isillustrated in FIG. 2 b and can be calculated as a linear equation usingthe following equation:

${{SOC}_{ref\_ lnr}(s)} = {{{SOC}\left( s_{0} \right)} - {\frac{DOD}{S_{temc}}s}}$

where s is the discrete trip distance 208, SOC(s₀) is the state ofcharge at the discrete trip distance at the beginning of the trip 200,DOD is the depth of discharge which equals SOC(s₀)−SOC(_(CS)) 202, andS_(temc)=S_(ccd)(1−k_(em)) is the trip energy management control tripdistance 210. K_(em) provides a distance buffer to ensure batterydepletion when reaching the recharge point 206, and S_(ccd) is the totalcharge cycle distance 204. This reference state of charge can also becalculated as a polynomial equation as illustrated in FIG. 2 c using thefollowing equation:

${{SOC}_{{ref\_}{apr}}(s)} = {{{\frac{\eta_{\delta}}{S_{temc}^{n - 1}}\left( {S_{temc} - s} \right)^{n}} + {k_{0}\left( {S_{temc} - s} \right)} + {{SOC}_{CS}k_{0}}} = {\frac{DOD}{S_{temc}} - \frac{\eta_{\delta}}{S_{temc}^{2}}}}$

where, η_(δ) is the difference between the two extreme values of η at amean discharge power level in the battery depletion SOC range. SOC_(cs)is the designed battery charge sustaining SOC level 202.

The preplanned piecewise linear and continuously calculated referencestate of charge at a discrete trip location (SOC_(ref)(s)) areillustrated in FIG. 2 d.

In FIG. 1, the feedback control point 124 can be implemented many ways,for example, one method can be where the feedback control point isdesigned evaluating the instantaneous SOC error (ε_(SOC)) as follows:Σ_(SOC)=sign(SOC_(ref)(s)−SOC(s))max(|SOC_(ref)(s)−SOC(s)|−SOC_(cb),0)

where SOC_(cb) defines an uncontrolled small vicinity around thereference SOC to reduce the control sensitivity. The change in energyconsumption ratio Δλ can be calculated in different ways, an example iscalculated in the spatial domain in the Battery Depletion Control block126 with the following equation:Δλ=λ(k)−λ(k−1)=Ctrl_(fb)(ε_(SOC))

where λ(k) is the value of lambda in spatial domain control stationkΔs≦s<(k+1)Δs. Δs is the station length and k is the unit ofquantization. The control strategy starts with λ(0) that ispre-calibrated with respect to the value of S_(ccd) and other previewtrip information if available. Ctrl_(fb) represents a set of feedbackcontrol functions that can be designed with different tracking controlmethodologies. Since the SOC profile is continuous and typically slow invariation, multiple different control strategies may be employed, forexample, a PID controller may be used for control with respect tobattery SOC profile tracking. In the PID controller, the integrationportion is trip domain integration and it is further limited with ananti-windup method. Another control strategy that may be used is a layerof fuzzy logic for variable control gain and control station lengthadjustments in order to adapt to different realistic driving patterns.

The value of λ is determined from a setpoint and adjusted according tothe Δλ in block 128. The resulting value of λ is utilized in the PowerSourcing Distribution Optimization (PSD) block 130 along with other datafrom the vehicle 110 and an operator 132, more details on this will beprovided in FIG. 5 and FIG. 6. The output of block 130 is P_(batt),which is utilized by the Energy Management Strategy (EMS) block 134.More details on block 134 will be provided in FIG. 7 and FIG. 8. Theoutput of the EMS block 134 controls the PCM 100.

There are two basic operation states of a PHEV: charge depleting mode(CD) where the state of charge stored in the battery is depleted at afaster rate than it is replenished and charge sustaining mode (CS) wherethe state of charge stored in the battery is replenished such that thetotal state of charge is generally maintained at a specific level. Thereis a charge-depletion-to-charge-sustaining transition that occurs whenthe vehicle operating in charge depleting mode reaches the chargesustaining level 202. At this point in time, the vehicle operating modetransitions from charge depletion mode to charge sustaining mode. In theexample shown in FIG. 2 a, a mostly charged PHEV (shown at a level whichis less than fully charged 200) is driven in the CD 212 state for thefirst part of the trip in which the battery's state of charge (SOC)exhibits a net decrease between SOC levels 200 and 202. Due to the lowercost of electricity compared to fuel, the available battery electricenergy is used for vehicle usage function before the next PHEV plug-inrecharge event to largely displace fuel consumption. Since knowledge ofthe occurrence of the next battery recharge event is usually unknown, bydefault, the PHEV operation starts with CD process to assure batterydepletion before the end of the trip.

During the CD operating state, according to the base PHEV energymanagement strategy, the battery's electric energy is used primarily topropel the vehicle, thereby maximally or near maximally depleting theelectric energy stored in the battery. By primarily utilizing thebattery energy to propel the vehicle early in the trip, the PHEV fuelconsumption is minimized when the trip distance is close to the PureElectric Range (PER) in EV/PEV (electric vehicle operation or in blendedoperation in which the internal combustion engine is used as little aspossible) operations. Fully-charged PHEVs have, for example, a 10-40mile PER in certain driving cycles, with the PER depending on the designgoals, the size of the battery pack, and driving cycles. During thedriving period shown as S_(ccd) 204 in FIG. 2 a, the vehicle isoperating in either the Maximum Charge Depletion (CD) 212 or the ChargeSustaining (CS) 214 state. At time zero 200, the controller operates thesystem at the maximum charge depletion rate 212 until the SOC reaches athreshold level 202. Upon reaching the threshold level 202, thecontroller operates the system in a charge sustaining state (CS) 214 forthe remainder of the trip S_(ccd) 204.

An alternative power management strategy for PHEVs has the potential toallocate driver power demand between the two propulsion devices in amore optimally balanced way than either maximum charge depletion orcharge sustaining states. During the driving period shown as S_(ccd)204, the vehicle is operating in either the Maximum Charge Depletion(CD) 216 or the Charge Sustaining (CS) 218 state (shown in FIG. 2 b,FIG. 2 c, and FIG. 2 d). By providing or assuming knowledge of the totaldistance that the PHEV plans to travel before the next battery chargeevent, the new PHEV battery operating state is extended resulting in aCharge Depletion (CD) state 216 (shown in FIG. 2 b, FIG. 2 c, and FIG. 2d), which extends the battery charge depletion state to the entireS_(ccd) 204 or to a segment S_(ccd)—Safe Margin 206 of a trip byreplacing the CD plus CS process (shown in FIG. 2 a). During theextended CD state 216, the PHEV powertrain is managed in a blendedoperating mode in which the engine and the battery are optimallycoordinated in satisfying the drive power demand (torque demand) basedon the control strategy examples provided in FIG. 2 b, FIG. 2 c, andFIG. 2 d. The drive power allocation to both energy sources isdynamically adjusted in order to achieve an optimum Energy ConsumptionRatio (ECR) that minimizes the Fuel Consumption Rate (FCR) with respectto the Electricity Depletion Rate (EDR). The fuel consumption rate maybe based on distance or time and the electricity depletion rate may bebased on distance or time. As a result, the battery SOC under thecontrolled process follows a desired reference SOC profile in thespatial domain (see FIGS. 2 b, 2 c, and 2 d). The reference SOC profilemay be designed with scalable trip information depending onavailability. At the minimal level, the drive distance betweenconsecutive battery charges is required. This battery SOC profile notonly assures battery depletion to the CS level before the end of tripS_(ccd), but also dictates the battery power along the trip by the ECRalong the reference SOC profile. The battery SOC profile can be designedfor fuel economy improvement, battery protection, battery durabilitymaximization, energy conservation, or the like. The battery SOC profilemay be linear (FIG. 2 b), curvilinear, polynomial (FIG. 2 c), stepped,or exhibit any other combination of declines and/or constant holds (FIG.2 d). Without loss of generality, the reference SOC profile primarilydiscussed in this disclosure is designed for fuel economy improvement byassuming charge cycle distance information knowledge a priori.

A power split PHEV configuration is illustrated in FIG. 3. This,however, is for example purposes only and not intended to be limiting asthe present disclosure applies to PHEVs of any suitable architecture.The control of vehicle 308 can have various configurations. In theexample shown in FIG. 3, a vehicle system controller 310 communicateswith a battery and battery control module 312, and a control module 366for a transmission 314. An engine 316, controlled by controller 310,distributes torque through torque input shaft 318 to transmission 314.

The transmission 314 includes a planetary gear unit 320, which comprisesa ring gear 322, a sun gear 324, and a planetary carrier assembly 326.The ring gear 322 distributes torque to step ratio gears comprisingmeshing gear elements 328, 330, 332, 334 and 336. A torque output shaft338 for the transaxle is drivably connected to vehicle traction wheels340 through a differential-and-axle mechanism 342.

Gears 330, 332 and 334 are mounted on a countershaft, the gear 332engaging a motor-driven gear 344. Electric motor 346 drives gear 344,which acts as a torque input for the countershaft gearing.

The battery of module 312 delivers electric power to the motor throughpower flow path 348. Generator 350 is connected electrically to thebattery and to the motor in known fashion, as shown at 352.

In FIG. 3, the vehicle system controller 310 receives input includingbut not limited to a transmission range selector input signal 360, anaccelerator pedal position sensor input signal 362, brake pedal positionsensor input signal 364, and distance until next recharge 358. Thevehicle system controller 310 outputs signals which are connectedelectrically to the engine 316, a transmission control module 366, andthe battery/BCM 312 in a known fashion, as shown at 368, 370, 372. VSC310 also outputs information to a driver information console 356 toinform the operator of the system operation.

As mentioned previously, there are two power sources for the driveline.The first power source is a combination of the engine and generatorsubsystems, which are connected together using the planetary gear unit320. The other power source involves only the electric drive systemincluding the motor, the generator and the battery, wherein the batteryacts as an energy storage medium for the generator and the motor.

As mentioned above, a plug-in hybrid electric vehicle (PHEV) is anextension of existing hybrid electric vehicle (HEV) technology, in whichan internal combustion engine is supplemented by an electric batterypack and electric machines to further gain increased mileage and reducedvehicle emissions. A PHEV utilizes a larger capacity battery pack than astandard hybrid vehicle and adds the capability to recharge the batteryfrom a standard electrical outlet to decrease onboard fuel consumptionto further improve the vehicle's fuel economy in the electric drivingmode or in the fuel/electricity blended driving mode. Referring to FIG.3, if HEV 308 is a PHEV, it includes a receptacle 380 which is connectedto the power grid or outside electrical source and coupled to battery312, possibly through a battery charger/converter 382.

In some applications, the vehicle further includes a system input oruser input device such as button 358, a keyboard, a wireless interfaceinput, or other input mechanism to provide to the VSC 310 theexpected/estimated driving distance to the station of the next batterycharge event. Furthermore, PHEV 308 may include a driver display 356that provides information, e.g., GPS output, as well as an interfaceinto which the driver may provide route information or requestadditional information. These information systems provide perceived orpredicted trip information, such as distance until next charge (DUC),driving patterns, driver power profile, etc., to the PHEV controlstrategy.

Conventional HEVs buffer fuel energy and recover kinematic energy inelectric form to improve the overall vehicle system operatingefficiency. The fuel is the only energy source. For PHEVs, there is anadditional source of energy—the amount of electric energy deposited inthe battery from the grid during battery charge events. A powermanagement strategy for PHEVs has the potential to allocate the drivepower demand between the two energy sources to achieve better fueleconomy or improved drivability while still satisfying the otherobjectives. While conventional HEVs are operated to maintain the batterystate of charge (SOC) around a constant level, PHEVs use as muchpre-saved battery electric (grid) energy as possible before the nextbattery charge event, i.e., it is desirable to fully use the relativelycheap grid supplied electric energy after each plug-in charge event.After the battery SOC depletes to a lowest conservative level, the PHEVoperates as a conventional HEV operating about the lowest conservativelevel for the battery.

FIG. 4 a illustrates a sample distribution of engine efficiency map andengine operation distribution based on a PHEV system utilizing astandard control strategy. The engine efficiency map and engineoperation distribution are plotted as a function of engine torque 400and engine speed 402. The engine speed is shown from 1000 rpm to 4000rpm and the engine torque is shown from 20 Nm to 180 Nm. In thisillustration, the engine is shown to cluster operation at multiplelocations; one cluster is around 2000 rpm when the engine torque isapproximately 40 Nm resulting in approximately 26% engine efficiency404. Also, at 2000 rpm there is a range of operation from the cluster at40 Nm 404 to the operation approximately at 120 Nm 406. Another clusterof operation occurs at approximately 1400 rpm when the engine torque isapproximately 70 Nm, resulting in approximately 31% engine efficiency408. A third cluster of operation occurs when the engine speed is 2500rpm and the engine torque is around 130 to 140 Nm, resulting inoperation efficiency of approximately 34% 410. During this standardoperation, the engine efficiency is shown to drop to as low as 24%.

FIG. 4 b illustrates a sample distribution of engine efficiency map andengine operation distribution based on a PHEV system utilizing theTrip-Oriented Energy Management Control Strategy (TEMC) controlstrategy. The engine efficiency map and engine operation distributionare plotted as a function of engine torque 400 and engine speed 402. Theengine efficiency follows contours 412 and the engine operation isplotted as a histogram in which a single point is a dark point whichgets lighter with increased occurrence 414. The engine speed is shownfrom 1000 rpm to 4000 rpm and the engine torque is shown from 20 Nm to180 Nm. In this illustration, the engine is shown to cluster operationaround 2400 rpm when the engine torque is approximately 120 Nm,resulting in approximately 34.7% engine efficiency 414. There are alsopoints of operation which are statistically insignificant as they areoutliers with a low occurrence 416. Using this TEMC control strategy,the operating efficiency is mainly focused around 34% efficient asillustrated by the high concentration of points at approximately 2500rpm and 120 Nm 414. This data illustrates the improved efficiency of theTEMC control strategy.

FIG. 5 illustrates an example embodiment for calculating the PowerSource Distribution Optimization 130 from FIG. 1. This exampleillustrates an example flow for generating a series of map tables whichcan be generated offline and then accessed quickly to improve run timeperformance or they can be calculated real-time. In FIG. 5, acombination of state points include but is not limited to the optimizedcostate λ 500, which can be either the instantaneous value of λ or itcan be a value of λ from a set of permissible values so that a table canbe calculated, vehicle speed V_(spd) 502, which can be either theinstantaneous value of V_(spd) or it can be a value of V_(spd) from aset of permissible values so that a table can be calculated, andoperator drive power request P_(whl) 504, which can be either theinstantaneous value of P_(whl) or it can be a value of P_(whl) from aset of permissible values so that a table can be calculated and SOC. Inblock 506, the Hamiltonian value H is set to an arbitrary high value,for example H=10, and a candidate battery power P_(batt) is chosen fromthe admissible battery sets 508, for example p=1.

The optimal engine speed and associated electrical power loss can becalculated in 510, or the value can be selected from a system operationoptimization table 512. In block 514, the fuel flow is calculated basedon variables including but not limited to engine speed and enginetorque, or the fuel flow can be determined via the use of a staticengine operation map 516.

The Hamiltonian is calculated using the following equation:

u * ⁡ ( s ) = arg ⁢ ⁢ min u ⁢ | ω eng * ∈ C ⁢ H ⁡ ( s ) ⁢ | λ = arg ⁢ ⁢ min u ⁢ |ω eng * ∈ C ⁢ ( m . f s ⁡ ( u ) + λ η ⁡ ( x ) ⁢ u Q batt ⁢ v veh ⁢ V oc h ) ⁢ |

where the PHEV power sourcing optimization has to be carried out withrespect to a given value of the Fuel Consumption Rate (FCR) toElectricity Depletion Rate (EDR), which is the Energy Consumption Rate(ECR) referred to as λ, u*(s) is the optimal trip domain battery powertrace, ω*_(eng)(s) is the corresponding system operating setpoint, thesteady state control input u(s)=P_(batt), η is defined as the equivalentbattery discharge power efficiency representing the useful battery powerratio to the total battery power consumption at degraded battery opencircuit voltage level, {dot over (m)}_(f) is the instantaneous fuel flowwhen the vehicle is moving, s is the trip domain distance variable,Q_(batt) is the high voltage battery capacity, V_(oc) is the batteryopen circuit voltage, and V^(h) _(oc) is the nominal battery opencircuit voltage at the highest SOC level.

Block 518 is a decision tree, if the Hamiltonian is less than or equalto the previous value, a different battery power setpoint is chosen 524along with the recalculation of τ_(eng) and m_(f) in blocks 510 and 514.If the Hamiltonian is less than the previous value, J2 is updated andthe optimal battery power is selected 520. A check is performed in block522 to complete all the candidate battery power sets. If all sets arecomplete, the Hamiltonian and P_(batt) with respect to a given V_(spd),P_(whl), λ, and SOC, are recorded. This calculation can be accomplishedreal-time, or it can be calculated across all sets V_(spd), P_(whl), andλ by calculating the result for all permissive values 526. The resultmay be stored in a series of map tables as illustrated in flow diagramFIG. 5 where the resultant series of maps are illustrated in FIG. 6.

FIG. 6 illustrates one method of determining Pbatt 610 based on a givenenergy consumption ratio λ 616. In this example, vehicle speed Vspd 612,drive power request Pwhl 614, and battery state of charge SOC 618 areused along with the energy consumption ratio λ 616 to calculate Pbatt610. In this example illustration, 6 maps are shown for 6 discretevalues of the energy consumption ratio λ 616 and the battery state ofcharge SOC 618. Other scenarios are also possible. The use of these mapsallows faster execution speed as these tables may be calibrated for aspecific system. Likewise, this data can be calculated real-time.

FIG. 7 illustrates an example for calculating the Energy ManagementStrategy 134 from FIG. 1. This illustrates an example flow forgenerating a series of map tables which can be generated offline andthen accessed quickly to improve run time performance. This flow,however, can also be performed in real-time. A combination of statepoints including but not limited to the vehicle speed 502, operatordriver power request 504, and battery power 700, where battery powerP_(batt) from the power sourcing distribution optimization block 130 areutilized. This calculation can be accomplished in real-time, or can becalculated across all sets V_(spd), P_(whl), and P_(batt) by calculatingthe result for all permissive values 724.

In block 702, a candidate engine speed setpoint from the admissibleengine speed set is chosen and the initial value of J_(I) is set tozero. Below is an example of an equation showing the relationshipbetween η, ω and J_(I):

${\max\limits_{\omega_{eng} \in C}J_{1}} = {\left. {\eta_{syn}\left( \omega_{eng} \right)} \right|_{\omega_{dft},r_{dft},P_{batt}} = \frac{P_{whl}}{P_{fuel} + P_{batt}}}$

Approximating the electronic power loss is illustrated in block 704,P_(epathloss)=P_(elecloss) 706, and the engine power is calculated inblock 708 in which the engine power can be solved using the followingequation:P _(eng) =P _(whl) −P _(batt) +P _(epathloss)

The generator torque, speed, and power loss (τ_(gen), ω_(gen), andP_(gen) _(—) _(loss)) and the motor torque, speed, and power loss(τ_(mot), ω_(mot), and P_(mot) _(—) _(loss)) can be calculated real-timeor offline based on the subsystem properties in blocks 710 and 712 usingthe following equations:P _(gen) _(—) _(loss) =f ₁ g(ω_(gen),τ_(gen))P _(mot) _(—) _(loss) =f ₁ m(ω_(mot),τ_(mot))

with ω representing rotational speed and

_(Q) representing the planetary gear ratio, the kinematic relationshipsare

$\omega_{gen} = {{\frac{\left( {1 + \varrho} \right)}{\varrho}\omega_{eng}} - {\frac{T_{1}T_{2}}{\varrho}\omega_{mot}}}$$\omega_{dft} = {\frac{T_{2}}{T_{g}}\omega_{mot}}$

In block 714, the electrical path power loss P_(epath) _(—) _(loss) isdetermined with the following relationship:

$\begin{matrix}{P_{epath\_ loss} = {P_{batt} - {\omega_{gen}\tau_{gen}} - {\omega_{mot}\tau_{mot}}}} \\{= {P_{mot\_ loss} + P_{gen\_ loss} + P_{elec\_ loss}}}\end{matrix}$

In block 716, the electric motor loss is evaluated to see if itconverges. If it is converging, J_(I), P_(epath) _(—) _(loss) andω_(eng) _(—) _(opt) can be updated. In block 718, the most efficientpower split state of both the electrical path and the mechanical path isdetermined by maximizing the output power for vehicle propulsion. Thisis performed by maximizing the inertia of the counter shaft bymaximizing the following equation:

${\max\limits_{\omega_{eng} \in C}J_{1}} = {\left. {\eta_{sys}\left( \omega_{eng} \right)} \right|_{\omega_{dft},\tau_{dft},P_{batt}} = \frac{P_{whl}}{P_{fuel} + P_{batt}}}$

This optimal power split state is determined using the optimal ω_(eng)and then minimizing the instantaneous fuel consumption with thefollowing equations:

$\mspace{20mu}{\left. \omega_{eng}^{*} \right|_{u,\tau_{dft},\omega_{dft}} = {{\arg\;{\min\limits_{\omega_{eng} \in C}{H(s)}}} = {\left. {\arg\;{\min\limits_{\omega_{eng} \in C}{\overset{.}{m}}_{f}^{s}}} \middle| {}_{u}\mspace{20mu} P_{ice\_ loss} \right. = {{P_{fuel}\left( {{\overset{.}{m}}_{f}\left( {\omega_{eng},\tau_{eng}} \right)} \right)} - P_{eng}}}}}$

This flow of equations can be calculated in real-time or can becalculated as table maps and stored in memory to reduce run-timecomputational load. If table maps are desired, the flow would followblock 720 and the flow within 722 for the complete set of permissiblevalues of ω_(eng). This calculation can be accomplished in real-time, orit can be calculated across all sets V_(spd), P_(whl), and P_(batt) bycalculating the result for all permissive values 724. The values forω_(eng) and P_(eng) are then saved and forwarded to the Powertraincontrol module 100.

FIG. 8 illustrates optimal engine speed ω_(eng) 810 as a function ofdrive power request P_(whl) 812, vehicle speed V_(spd) 814, and batterypower P_(batt) 616. This can be calculated in real-time or can becalculated for a specific battery power P_(batt) 616 as illustrated. Theuse of these maps allows faster execution speed as these tables may becalibrated for a specific system.

The mechanical power transfer loss is not relevant in this calculationas the internal combustion engine loss P_(ice-loss) and the electricalpower transfer loss P_(epath-loss) dominate the total power loss in PHEVoperations. In the above equations, P_(whl) is the drive power requestat wheels, P_(fuel) is the total power supplied from the fuel at currentfuel flow rate, P_(batt) is the battery power that takes positive signfor discharge and negative sign for charge, and P_(loss) is the PHEVsystem power transfer loss. The overall PHEV system's operating point isexternally determined by the driveshaft torque τ_(dft) that ispropelling the vehicle at the current driveshaft rotational speedω_(dft).

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms of the invention. Rather,the words used in the specification are words of description rather thanlimitation, and it is understood that various changes may be madewithout departing from the spirit and scope of the invention.Additionally, the features of various implementing embodiments may becombined to form further embodiments of the invention.

What is claimed is:
 1. A plug-in hybrid electric vehicle comprising: anengine; an electric machine having a torque limit; a battery having acharge-depletion-to-charge-sustaining transition threshold and aninitial state of charge at a beginning of a trip greater than thethreshold; and at least one controller programmed to operate the engineand electric machine such that a state of charge of the battery achievesapproximately the charge-depletion-to-charge-sustaining transitionthreshold after the vehicle has been driven, during the trip, a distancegreater than a specified distance, wherein the specified distance is atotal distance the vehicle could have been driven exclusively by theelectric machine until the state of charge achieves approximately thecharge-depletion-to-charge-sustaining transition threshold while a totaltorque demand remains less than or equal to the torque limit, andaccording to a plurality of specified ratios of fuel consumption ratefor the engine to energy consumption rate for the electric machine suchthat the state of charge achieves approximately thecharge-depletion-to-charge-sustaining transition threshold after thevehicle has been driven the distance greater than the specifieddistance, and wherein the specified ratios of fuel consumption rate forthe engine to enemy consumption rate for the electric machine correspondto a plurality of segments of the trip.
 2. The vehicle of claim 1wherein the at least one controller is further programmed to operate theelectric machine and battery in charge depletion mode while the vehicleis being driven the distance greater than the specified distance.
 3. Thevehicle of claim 1 wherein each of the specified ratios of fuelconsumption rate for the engine to enemy consumption rate for theelectric machine is based on at least one characteristic of thecorresponding segment of the trip.
 4. The vehicle of claim 3 wherein theat least one characteristic includes road type, road grade, postedspeed, traffic, day of the week, or time of day.
 5. A method forcontrolling a vehicle comprising: operating an engine and electricmachine, by a vehicle system controller, based on a plurality ofspecified ratios of engine fuel consumption rate to electric machineenergy consumption rate such that a state of charge of a batterygenerally decreases and then achieves approximately acharge-depletion-to-charge-sustaining transition threshold after thevehicle has been driven a distance greater than the vehicle's pureelectrical range.
 6. The method of claim 5 further comprising operatingthe engine and electric machine such that the state of charge achievesapproximately the charge-depletion-to-charge-sustaining transitionthreshold near the end of the vehicle's route but not before.
 7. Avehicle comprising: an engine; an electric machine; a battery; and atleast one controller programmed to start or stop the engine according toa specified ratio of fuel consumption rate for the engine to energyconsumption rate for the electric machine for each of a plurality ofsegments of a route such that a state of charge of the battery generallydecreases at a desired rate until the state of charge achieves acharge-depletion-to-charge-sustaining transition threshold and to, inresponse to the state of charge achieving thecharge-depletion-to-charge-sustaining transition threshold, operate theengine to maintain the state of charge at approximately thecharge-depletion-to-charge-sustaining transition threshold for a balanceof the route.
 8. The vehicle of claim 7 wherein the plurality ofsegments defines a distance greater than a total distance the vehiclecould have been driven exclusively by the electric machine until thestate of charge achieves approximately thecharge-depletion-to-charge-sustaining transition threshold while a totaltorque demand remains less than or equal to a torque limit of theelectric machine.
 9. The vehicle of claim 7 wherein the plurality ofsegments defines a distance greater than a pure electrical range of thevehicle.
 10. The vehicle of claim 7 wherein the at least one controlleris further programmed to start or stop the engine such that the state ofcharge achieves approximately the charge-depletion-to-charge-sustainingtransition threshold near the end of the route.
 11. The vehicle of claim7 wherein each of the specified ratios of fuel consumption rate for theengine to energy consumption rate for the electric machine is based onat least one characteristic of the corresponding segment of the route.12. The vehicle of claim 11 wherein the at least one characteristicincludes road grade, posted speed, traffic, day of the week, or time ofday.